Py Torch

return len(self.queries) # Load pre-trained model and tokenizer model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Crea…

accuracy = accuracy_score(test_df['label'], predicted_labels) print(f"Accuracy for {model_name}: {accuracy:.2f}") return accuracy # List of models to experiment with models_to_test = [ "bert-base-uncased", "roberta-bas…

scaler.update() optimizer.zero_grad() # Example usage: train_model_with_amp(model, optimizer, dataloader, device, gradient_accumulation_steps=4) ``` 4. **Data Loading Efficiency:** - Use effici…

Use PyTorch to fuse the scores from sparse and dense searches: ```python def fuse_scores(sparse_scores, dense_scores, sparse_weight=0.5, dense_weight=0.5): # Convert scores to PyTorch tensors sparse_scores_tensor = torch.tensor(spa…

query = request.json['query'] results = es.search(index="documents", body={"query": {"match": {"text": query}}}) return jsonify(results) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000) ``` - **Den…

- Use `torch.cuda.amp` to enable mixed precision training with `GradScaler` and `autocast`. ### Additional Considerations - **Batch Size**: Adjust the batch size based on the available VRAM. For example, if your GPU has 16 GB of VRAM, …

average_latency = total_time / num_batches print(f"Total time: {total_time:.4f} seconds") print(f"Average latency per batch: {average_latency:.4f} seconds") # Example output for a single batch print(optimized_input_ids, optimized_attentio…

Your current model architecture is quite simple. Depending on the complexity of your data, you might need a more sophisticated model. However, for now, let's focus on optimizing the existing architecture. ### 3. Hyperparameter Tuning Exper…

model = torch.nn.Module() # Define the LLM call function def llm_call(query): # Perform the LLM call output = model(query) return output # Test the function with 500 queries per second queries = [...] # list of 500 queries fo…

```python def save_model(version, data): try: # Save model to database db.save(version, data) except VersionConflictError as e: # Log error and retry save logging.error(f"Version conflict error: {e}")…

3. **Increase Model Depth**: Adding more layers can help capture more complex patterns in the data. 4. **Adjust Learning Rate**: Fine-tuning the learning rate can help achieve better convergence. 5. **Use Weight Decay (L2 Regularization)**:…

```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset # Example data preparation inputs = torch.randn(3000, 128) # Example input data labels = torch.randn(3000, 1) …

return x # Initialize the model and optimizer model = MyModel() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # Define the update logic def update_model(model, optimizer, data): # Update the model using the data …